Data Analyst - free course from Otus, training, Date: December 5, 2023.

Alexandra has been working in the field of analytics and BI since 2019. By this time, she received a bachelor's degree in Software Engineering from St. Petersburg State University of Aviation Administration, and then a master's degree. The first steps in his career were taken at the American company Intermedia Cloud Communications as a junior data analyst, and by 2021 he managed to become the head of the analytics team. This whole year was devoted to a new cross-team project for international financial management on the Microsoft stack (MS SQL Server, SSRS, SSIS, Power BI). Since March 2022, he has been working in the Tinkoff Bank group of companies as a warehouse analyst data. Provides support to top management of the financial department in building prototypes of ETL processes using Greenplum, ad-hoc analytics in Python, reporting and visualization in Tableau. In 2020, she received additional education in the direction of Project Management Manager in IT. He is a staunch supporter of flexible development methodologies. Believes that the most profitable investments are investments in one's own development. Stack: SQL, SAS DIS, SSIS, Tableau, Power BI, Python

For 5 years in IT, she worked as an HR analyst and Business intelligence specialist at Luxoft, and is now a specialist in analytics and reporting visualization at Exness. An economist by training. Stack: Tableau Desktop & Server, Data analysis & visualization, SQL. In my work, I look for a healthy balance between writing a good data source and creating a beautiful visual.

8 years of corporate experience in analytics. SQL, Tableau, c++, python. Created analytical and product solutions in large companies such as MTS, Ozon, ivi.ru Worked in product teams in Russia, Germany, Poland. Teacher

Introduction to Data Analysis and Basic Statistics

-Topic 1. General population and sample, levels of measurement
-Topic 2. Normal distribution, level of statistical significance, standard deviation. Central limit theorem. Confidence intervals and standard error
-Topic 3. Descriptive statistics. Measure of central tendency
-Topic 4. Normal distribution, level of statistical significance, standard deviation. Central limit theorem
-Topic 5. Confidence intervals and standard error
-Topic 6. Level of significance, statistical hypotheses
-Topic 7. Correlation coefficient
-Topic 8.Methods of data comparison. Comparison of nominal data.
-Topic 9. Methods for comparing averages

DBMS and SQL

-Topic 10.Introduction to relational databases. Row and Columnar Databases
-Topic 11.Creating and editing tables. DDL. DML, DCL
-Topic 12. Data selection, conditions, data slices in SQL
-Topic 13. Aggregating functions. Grouping and sorting data
-Topic 14. Nested queries and temporary tables
-Topic 15.Types of table joins
-Topic 16. Expressions in SQL
-Topic 17.Built-in functions in SQL
-Topic 18.Database objects. Tables and views. Indexes and partitions
-Topic 19. Query plan and performance optimization

Introduction to Python

-Topic 20.Introduction to syntax. Jupyter Notebook
-Topic 21. Variables and data types. Data output and arithmetic operations
-Topic 22.Python Basics. Operators, loops
-Topic 23.Python data structures. Strings, Lists and Tuples and Dictionaries
-Topic 24.For and while loops
-Topic 25.Functions, modules and libraries
-Topic 26. Libraries NumPy, pandas, SciPy
-Topic 27.Visualization methods. Basics of matplotlib, seaborn, plotly

Data preprocessing, exploratory and statistical data analysis

-Topic 28.Working with omissions and duplicates
-Topic 29. Categorization of data
-Topic 30. Data type conversion
-Topic 31. Data normalization
-Topic 32. Data categorization
-Topic 33. Time series analysis
-Topic 34. Studying data slices
-Topic 35.Data relationships
-Topic 36.Validation of results
-Topic 37. Statement and testing of hypotheses

Introduction to Business Intelligence and Visual Data Analysis

-Topic 38.Introduction to Business Intelligence
-Topic 39: Tableau Desktop/Public Ecosystem Overview
-Topic 40. Main types of data sources in Tableau, connections
-Topic 41. Tableau Desktop interface and basic operating concepts
-Topic 42.Visualization: diagrams, main scenarios for their use
-Topic 43. Pre-installed and custom calculations
-Topic 44.Organizing data in Tableau
-Topic 45.Order of operations in Tableau
-Topic 46.Introduction to information design
-Topic 47. How user perception works
-Topic 48. Main mistakes when creating dashboards
-Topic 49.Dashboard design
-Topic 50. Layout for various tasks and devices
-Topic 51.Planning user interaction with the dashboard

Project life cycle in data analysis

-Topic 52. Data-driven decision making in business
-Topic 53. Gathering requirements
-Topic 54. Crystallization of requirements and creation of a prototype
-Topic 55. Iterative work with the customer at the development stage
-Topic 56.Demo of the finished version and user testing stage
-Topic 57. Release and post-production
-Topic 58. Monitoring demand and receiving feedback

Special methods and areas in data analytics

-Topic 59.Analysis of business indicators
-Topic 60. Product analytics, unit economics, A/B tests
-Topic 61. Metrics and funnels, hierarchy of metrics
-Topic 62. Cohort analysis
-Topic 63.BI analytics
-Topic 64.Data journalism